What is Bag-of-Words?

Bag-of-words is useful for representing textual data in a passage when using text for training and modelling in Machine Learning. We represent the text in the form of numbers generally in Machine Learning. BOW allows to extract features from text using numerous ways to convert text into numbers. It provides two main features:

1. Known vocabulary of words: BOW relies on a predefined vocabulary of words. Each unique word in the corpus is assigned a unique identifier.
2. Frequency or probability of occurrence of words: BOW captures the frequency or probability of occurrence of each word in a document. This information is used to create a numerical representation of the text.

With the help of a bag of words, we can detect the type of document, useful for sentimental analysis, document classification, and spam filtering.

The BOW model treats each sentence as a vector, where each element of the vector corresponds to the frequency of a word in the dictionary converting a collection of text documents into a matrix, where each row represents a document, and each column represents a unique word.

But, BOW does not preserve the structure of sentences or consider word order. It treats each word as independent, ignoring semantic relationships.

Bag-of-Words Example

Suppose we have the following two sentences:

1. This is my car.
2. My car is red in colour.

So we would have a dictionary of some words and we track the frequency of words of each sentence.

With the frequency table, we can feed this vector into machine learning models and train them.

Effectively representing textual data is crucial for training models in Machine Learning. The Bag-of-Words (BOW) model serves this purpose by transforming text into numerical form. This article comprehensively explores the Bag-of-Words model, elucidating its fundamental concepts and utility in text representation for Machine Learning.